Emergency-Aware Traffic Signal Control System using Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802125Keywords:
Emergency Vehicle Detection, Traffic Signal Control, Machine Learning, YOLO Algorithm, , Smart Traffic System, Computer Vision, Raspberry PiAbstract
Urban traffic congestion significantly delays emergency vehicles such as ambulances and fire trucks, leading to critical loss of time and potential loss of life. Traditional traffic signal systems operate on fixed timing mechanisms and lack the ability to dynamically adapt to real-time traffic conditions. This paper presents an Emergency-Aware Traffic Signal Control System using Machine Learning, designed to prioritize emergency vehicles and optimize traffic flow.
The proposed system utilizes camera-based surveillance and a deep learning model (YOLO) for real-time detection of emergency vehicles and traffic density. Upon detecting an emergency vehicle, the system automatically switches the corresponding traffic signal to green, ensuring a clear path. In the absence of emergency vehicles, traffic signals are dynamically controlled based on vehicle density.
The system is implemented using Raspberry Pi 5 for real-time processing, along with OpenCV for image processing and Python-based machine learning frameworks. This approach reduces dependency on expensive GPS or RFID systems while improving scalability and efficiency. The proposed system aims to enhance emergency response time, reduce congestion, and contribute to smart city infrastructure.
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